Overview

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Dataset statistics

Number of variables56
Number of observations912
Missing cells0
Missing cells (%)0.0%
Duplicate rows48
Duplicate rows (%)5.3%
Total size in memory115.0 KiB
Average record size in memory129.1 B

Variable types

Numeric8
Categorical3
Boolean45

Alerts

Cpu_Cores has constant value "0" Constant
Dataset has 48 (5.3%) duplicate rowsDuplicates
Company_Apple is highly overall correlated with Gpu_Model and 3 other fieldsHigh correlation
Company_Huawei is highly overall correlated with ResolutionHigh correlation
Company_Microsoft is highly overall correlated with OpSys_Windows 10 SHigh correlation
Company_Samsung is highly overall correlated with InchesHigh correlation
Cpu_Family_Intel is highly overall correlated with Gpu_Family_radeonHigh correlation
Cpu_Frequency is highly overall correlated with Cpu_Series_i3 and 7 other fieldsHigh correlation
Cpu_Series_i3 is highly overall correlated with Cpu_FrequencyHigh correlation
Cpu_Series_i5 is highly overall correlated with Cpu_Frequency and 1 other fieldsHigh correlation
Cpu_Series_i7 is highly overall correlated with Cpu_Frequency and 2 other fieldsHigh correlation
Flash_Storage is highly overall correlated with InchesHigh correlation
Gpu_Brand_Intel is highly overall correlated with Cpu_Frequency and 5 other fieldsHigh correlation
Gpu_Brand_NVIDIA is highly overall correlated with Cpu_Frequency and 6 other fieldsHigh correlation
Gpu_Family_gtx is highly overall correlated with Cpu_Frequency and 5 other fieldsHigh correlation
Gpu_Family_hd graphics is highly overall correlated with Cpu_Frequency and 5 other fieldsHigh correlation
Gpu_Family_iris is highly overall correlated with ResolutionHigh correlation
Gpu_Family_radeon is highly overall correlated with Cpu_Family_IntelHigh correlation
Gpu_Model is highly overall correlated with Company_Apple and 1 other fieldsHigh correlation
Inches is highly overall correlated with Company_Samsung and 9 other fieldsHigh correlation
Memory_HDD is highly overall correlated with Inches and 2 other fieldsHigh correlation
Memory_SSD is highly overall correlated with Memory_HDD and 2 other fieldsHigh correlation
OpSys_Android is highly overall correlated with InchesHigh correlation
OpSys_Mac OS X is highly overall correlated with Company_Apple and 1 other fieldsHigh correlation
OpSys_Windows 10 S is highly overall correlated with Company_MicrosoftHigh correlation
OpSys_macOS is highly overall correlated with Company_Apple and 1 other fieldsHigh correlation
Ram is highly overall correlated with Cpu_Series_i7 and 2 other fieldsHigh correlation
Resolution is highly overall correlated with Company_Apple and 5 other fieldsHigh correlation
TypeName_Gaming is highly overall correlated with Cpu_Frequency and 3 other fieldsHigh correlation
TypeName_Netbook is highly overall correlated with InchesHigh correlation
TypeName_Notebook is highly overall correlated with WeightHigh correlation
TypeName_Ultrabook is highly overall correlated with Inches and 1 other fieldsHigh correlation
Weight is highly overall correlated with Gpu_Brand_Intel and 8 other fieldsHigh correlation
Flash_Storage is highly imbalanced (68.9%) Imbalance
Hybrid is highly imbalanced (92.7%) Imbalance
Cpu_Family_Intel is highly imbalanced (71.2%) Imbalance
Cpu_Series_ryzen 1 is highly imbalanced (96.8%) Imbalance
Gpu_Family_iris is highly imbalanced (89.2%) Imbalance
Company_Acer is highly imbalanced (58.6%) Imbalance
Company_Apple is highly imbalanced (86.6%) Imbalance
Company_Chuwi is highly imbalanced (98.8%) Imbalance
Company_Fujitsu is highly imbalanced (98.8%) Imbalance
Company_Google is highly imbalanced (98.8%) Imbalance
Company_Huawei is highly imbalanced (97.7%) Imbalance
Company_LG is highly imbalanced (98.8%) Imbalance
Company_MSI is highly imbalanced (76.0%) Imbalance
Company_Mediacom is highly imbalanced (94.3%) Imbalance
Company_Microsoft is highly imbalanced (95.9%) Imbalance
Company_Razer is highly imbalanced (95.9%) Imbalance
Company_Samsung is highly imbalanced (95.1%) Imbalance
Company_Toshiba is highly imbalanced (75.5%) Imbalance
Company_Vero is highly imbalanced (98.8%) Imbalance
Company_Xiaomi is highly imbalanced (97.7%) Imbalance
TypeName_2 in 1 Convertible is highly imbalanced (55.7%) Imbalance
TypeName_Netbook is highly imbalanced (86.6%) Imbalance
TypeName_Workstation is highly imbalanced (83.6%) Imbalance
OpSys_Android is highly imbalanced (97.7%) Imbalance
OpSys_Chrome OS is highly imbalanced (86.0%) Imbalance
OpSys_Linux is highly imbalanced (71.2%) Imbalance
OpSys_Mac OS X is highly imbalanced (94.3%) Imbalance
OpSys_No OS is highly imbalanced (71.2%) Imbalance
OpSys_Windows 10 S is highly imbalanced (95.1%) Imbalance
OpSys_Windows 7 is highly imbalanced (77.5%) Imbalance
OpSys_macOS is highly imbalanced (90.6%) Imbalance
Memory_SSD has 328 (36.0%) zeros Zeros
Memory_HDD has 499 (54.7%) zeros Zeros
Gpu_Model has 244 (26.8%) zeros Zeros

Reproduction

Analysis started2024-11-29 09:04:11.708889
Analysis finished2024-11-29 09:04:23.568832
Duration11.86 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Inches
Real number (ℝ)

High correlation 

Distinct17
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.060746
Minimum10.1
Maximum18.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2024-11-29T10:04:23.661417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10.1
5-th percentile12.5
Q114
median15.6
Q315.6
95-th percentile17.3
Maximum18.4
Range8.3
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.412363
Coefficient of variation (CV)0.093777759
Kurtosis0.051540077
Mean15.060746
Median Absolute Deviation (MAD)0
Skewness-0.49546511
Sum13735.4
Variance1.9947691
MonotonicityNot monotonic
2024-11-29T10:04:23.756600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
15.6 481
52.7%
14 129
 
14.1%
17.3 116
 
12.7%
13.3 113
 
12.4%
12.5 26
 
2.9%
11.6 21
 
2.3%
12 4
 
0.4%
13.5 4
 
0.4%
15.4 3
 
0.3%
13.9 3
 
0.3%
Other values (7) 12
 
1.3%
ValueCountFrequency (%)
10.1 3
 
0.3%
11.3 1
 
0.1%
11.6 21
 
2.3%
12 4
 
0.4%
12.3 2
 
0.2%
12.5 26
 
2.9%
13 2
 
0.2%
13.3 113
12.4%
13.5 4
 
0.4%
13.9 3
 
0.3%
ValueCountFrequency (%)
18.4 1
 
0.1%
17.3 116
 
12.7%
17 1
 
0.1%
15.6 481
52.7%
15.4 3
 
0.3%
15 2
 
0.2%
14 129
 
14.1%
13.9 3
 
0.3%
13.5 4
 
0.4%
13.3 113
 
12.4%

Ram
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3574561
Minimum2
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-11-29T10:04:23.841117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q14
median8
Q38
95-th percentile16
Maximum64
Range62
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.1086407
Coefficient of variation (CV)0.61126743
Kurtosis19.188219
Mean8.3574561
Median Absolute Deviation (MAD)2
Skewness2.9613873
Sum7622
Variance26.09821
MonotonicityNot monotonic
2024-11-29T10:04:23.932743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
8 428
46.9%
4 266
29.2%
16 140
 
15.4%
6 31
 
3.4%
12 20
 
2.2%
2 14
 
1.5%
32 11
 
1.2%
24 1
 
0.1%
64 1
 
0.1%
ValueCountFrequency (%)
2 14
 
1.5%
4 266
29.2%
6 31
 
3.4%
8 428
46.9%
12 20
 
2.2%
16 140
 
15.4%
24 1
 
0.1%
32 11
 
1.2%
64 1
 
0.1%
ValueCountFrequency (%)
64 1
 
0.1%
32 11
 
1.2%
24 1
 
0.1%
16 140
 
15.4%
12 20
 
2.2%
8 428
46.9%
6 31
 
3.4%
4 266
29.2%
2 14
 
1.5%

Weight
Real number (ℝ)

High correlation 

Distinct157
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0536579
Minimum0.69
Maximum4.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2024-11-29T10:04:24.044023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.69
5-th percentile1.1755
Q11.5575
median2.06
Q32.31
95-th percentile3.2
Maximum4.7
Range4.01
Interquartile range (IQR)0.7525

Descriptive statistics

Standard deviation0.65950225
Coefficient of variation (CV)0.3211354
Kurtosis2.4836035
Mean2.0536579
Median Absolute Deviation (MAD)0.36
Skewness1.1244477
Sum1872.936
Variance0.43494321
MonotonicityNot monotonic
2024-11-29T10:04:24.173150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.2 88
 
9.6%
2.1 44
 
4.8%
2 33
 
3.6%
2.3 32
 
3.5%
2.4 32
 
3.5%
2.5 21
 
2.3%
2.8 20
 
2.2%
2.18 19
 
2.1%
1.9 18
 
2.0%
2.04 16
 
1.8%
Other values (147) 589
64.6%
ValueCountFrequency (%)
0.69 3
 
0.3%
0.81 1
 
0.1%
0.92 4
 
0.4%
0.97 1
 
0.1%
0.99 1
 
0.1%
1.05 7
0.8%
1.08 1
 
0.1%
1.09 2
 
0.2%
1.1 11
1.2%
1.11 2
 
0.2%
ValueCountFrequency (%)
4.7 1
 
0.1%
4.6 2
 
0.2%
4.5 1
 
0.1%
4.42 7
0.8%
4.4 1
 
0.1%
4.36 4
0.4%
4.3 2
 
0.2%
4.2 2
 
0.2%
4.14 3
0.3%
4 1
 
0.1%

Resolution
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2157365.5
Minimum1049088
Maximum8294400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2024-11-29T10:04:24.289238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1049088
5-th percentile1049088
Q11440000
median2073600
Q32073600
95-th percentile5760000
Maximum8294400
Range7245312
Interquartile range (IQR)633600

Descriptive statistics

Standard deviation1386116.9
Coefficient of variation (CV)0.64250445
Kurtosis10.5671
Mean2157365.5
Median Absolute Deviation (MAD)0
Skewness3.1183818
Sum1.9675173 × 109
Variance1.9213201 × 1012
MonotonicityNot monotonic
2024-11-29T10:04:24.390776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2073600 581
63.7%
1049088 224
 
24.6%
8294400 29
 
3.2%
5760000 20
 
2.2%
3686400 15
 
1.6%
1440000 15
 
1.6%
4096000 6
 
0.7%
3317760 4
 
0.4%
3393024 4
 
0.4%
2304000 4
 
0.4%
Other values (5) 10
 
1.1%
ValueCountFrequency (%)
1049088 224
 
24.6%
1296000 3
 
0.3%
1440000 15
 
1.6%
2073600 581
63.7%
2304000 4
 
0.4%
3110400 2
 
0.2%
3317760 4
 
0.4%
3393024 4
 
0.4%
3686400 15
 
1.6%
3840000 1
 
0.1%
ValueCountFrequency (%)
8294400 29
3.2%
5760000 20
2.2%
5184000 3
 
0.3%
4990464 1
 
0.1%
4096000 6
 
0.7%
3840000 1
 
0.1%
3686400 15
1.6%
3393024 4
 
0.4%
3317760 4
 
0.4%
3110400 2
 
0.2%

Memory_SSD
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182.78509
Minimum0
Maximum1024
Zeros328
Zeros (%)36.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2024-11-29T10:04:24.493958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median210
Q3256
95-th percentile512
Maximum1024
Range1024
Interquartile range (IQR)256

Descriptive statistics

Standard deviation189.15069
Coefficient of variation (CV)1.0348256
Kurtosis3.5810106
Mean182.78509
Median Absolute Deviation (MAD)82
Skewness1.4394479
Sum166700
Variance35777.982
MonotonicityNot monotonic
2024-11-29T10:04:24.597868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
256 346
37.9%
0 328
36.0%
128 119
 
13.0%
512 96
 
10.5%
1024 12
 
1.3%
32 6
 
0.7%
180 1
 
0.1%
768 1
 
0.1%
64 1
 
0.1%
240 1
 
0.1%
ValueCountFrequency (%)
0 328
36.0%
8 1
 
0.1%
32 6
 
0.7%
64 1
 
0.1%
128 119
 
13.0%
180 1
 
0.1%
240 1
 
0.1%
256 346
37.9%
512 96
 
10.5%
768 1
 
0.1%
ValueCountFrequency (%)
1024 12
 
1.3%
768 1
 
0.1%
512 96
 
10.5%
256 346
37.9%
240 1
 
0.1%
180 1
 
0.1%
128 119
 
13.0%
64 1
 
0.1%
32 6
 
0.7%
8 1
 
0.1%

Memory_HDD
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean437.48684
Minimum0
Maximum2048
Zeros499
Zeros (%)54.7%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2024-11-29T10:04:24.753091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31024
95-th percentile1024
Maximum2048
Range2048
Interquartile range (IQR)1024

Descriptive statistics

Standard deviation533.51533
Coefficient of variation (CV)1.2195003
Kurtosis-0.16404952
Mean437.48684
Median Absolute Deviation (MAD)0
Skewness0.8327219
Sum398988
Variance284638.61
MonotonicityNot monotonic
2024-11-29T10:04:24.866036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 499
54.7%
1024 301
33.0%
500 87
 
9.5%
2048 23
 
2.5%
128 1
 
0.1%
32 1
 
0.1%
ValueCountFrequency (%)
0 499
54.7%
32 1
 
0.1%
128 1
 
0.1%
500 87
 
9.5%
1024 301
33.0%
2048 23
 
2.5%
ValueCountFrequency (%)
2048 23
 
2.5%
1024 301
33.0%
500 87
 
9.5%
128 1
 
0.1%
32 1
 
0.1%
0 499
54.7%

Flash_Storage
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size51.8 KiB
0
861 
1
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters912
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 861
94.4%
1 51
 
5.6%

Length

2024-11-29T10:04:25.000166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-29T10:04:25.140327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 861
94.4%
1 51
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 861
94.4%
1 51
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 912
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 861
94.4%
1 51
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 912
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 861
94.4%
1 51
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 912
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 861
94.4%
1 51
 
5.6%

Hybrid
Categorical

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size51.8 KiB
0
904 
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters912
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 904
99.1%
1 8
 
0.9%

Length

2024-11-29T10:04:25.234765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-29T10:04:25.310306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 904
99.1%
1 8
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 904
99.1%
1 8
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 912
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 904
99.1%
1 8
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 912
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 904
99.1%
1 8
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 912
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 904
99.1%
1 8
 
0.9%

Cpu_Cores
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size51.8 KiB
0
912 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters912
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 912
100.0%

Length

2024-11-29T10:04:25.618228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-29T10:04:25.691773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 912
100.0%

Most occurring characters

ValueCountFrequency (%)
0 912
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 912
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 912
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 912
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 912
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 912
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 912
100.0%

Cpu_Frequency
Real number (ℝ)

High correlation 

Distinct24
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3166228
Minimum0.9
Maximum3.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2024-11-29T10:04:25.774431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1.2
Q12
median2.5
Q32.7
95-th percentile2.8
Maximum3.6
Range2.7
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.50161101
Coefficient of variation (CV)0.21652684
Kurtosis0.022667009
Mean2.3166228
Median Absolute Deviation (MAD)0.2
Skewness-0.83972499
Sum2112.76
Variance0.25161361
MonotonicityNot monotonic
2024-11-29T10:04:25.879174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2.5 213
23.4%
2.8 120
13.2%
2.7 108
11.8%
1.6 85
 
9.3%
2 69
 
7.6%
2.3 55
 
6.0%
1.8 50
 
5.5%
2.6 50
 
5.5%
2.4 40
 
4.4%
1.1 34
 
3.7%
Other values (14) 88
9.6%
ValueCountFrequency (%)
0.9 3
 
0.3%
1.1 34
 
3.7%
1.2 12
 
1.3%
1.3 5
 
0.5%
1.44 6
 
0.7%
1.5 6
 
0.7%
1.6 85
9.3%
1.8 50
5.5%
1.9 2
 
0.2%
1.92 1
 
0.1%
ValueCountFrequency (%)
3.6 5
 
0.5%
3.2 1
 
0.1%
3.1 3
 
0.3%
3 16
 
1.8%
2.9 17
 
1.9%
2.8 120
13.2%
2.7 108
11.8%
2.6 50
 
5.5%
2.5 213
23.4%
2.4 40
 
4.4%

Gpu_Model
Real number (ℝ)

High correlation  Zeros 

Distinct29
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean512.61513
Minimum0
Maximum6000
Zeros244
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2024-11-29T10:04:25.977650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median520
Q3620
95-th percentile1060
Maximum6000
Range6000
Interquartile range (IQR)620

Descriptive statistics

Standard deviation524.18918
Coefficient of variation (CV)1.0225784
Kurtosis58.172067
Mean512.61513
Median Absolute Deviation (MAD)100
Skewness5.7990563
Sum467505
Variance274774.3
MonotonicityNot monotonic
2024-11-29T10:04:26.086452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 244
26.8%
620 240
26.3%
520 146
16.0%
1050 63
 
6.9%
1060 39
 
4.3%
500 28
 
3.1%
530 27
 
3.0%
400 25
 
2.7%
1070 24
 
2.6%
515 11
 
1.2%
Other values (19) 65
 
7.1%
ValueCountFrequency (%)
0 244
26.8%
70 1
 
0.1%
400 25
 
2.7%
405 5
 
0.5%
430 1
 
0.1%
500 28
 
3.1%
505 7
 
0.8%
510 3
 
0.3%
515 11
 
1.2%
520 146
16.0%
ValueCountFrequency (%)
6000 4
 
0.4%
5300 1
 
0.1%
1080 3
 
0.3%
1070 24
 
2.6%
1060 39
4.3%
1050 63
6.9%
980 2
 
0.2%
960 2
 
0.2%
650 2
 
0.2%
640 8
 
0.9%

Cpu_Family_Intel
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
True
866 
False
 
46
ValueCountFrequency (%)
True 866
95.0%
False 46
 
5.0%
2024-11-29T10:04:26.175015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Cpu_Series_i3
Boolean

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
808 
True
104 
ValueCountFrequency (%)
False 808
88.6%
True 104
 
11.4%
2024-11-29T10:04:26.243555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Cpu_Series_i5
Boolean

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
617 
True
295 
ValueCountFrequency (%)
False 617
67.7%
True 295
32.3%
2024-11-29T10:04:26.313194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Cpu_Series_i7
Boolean

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
546 
True
366 
ValueCountFrequency (%)
False 546
59.9%
True 366
40.1%
2024-11-29T10:04:26.382152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Cpu_Series_ryzen 1
Boolean

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
909 
True
 
3
ValueCountFrequency (%)
False 909
99.7%
True 3
 
0.3%
2024-11-29T10:04:26.450145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Gpu_Brand_Intel
Boolean

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
True
498 
False
414 
ValueCountFrequency (%)
True 498
54.6%
False 414
45.4%
2024-11-29T10:04:26.519555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Gpu_Brand_NVIDIA
Boolean

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
630 
True
282 
ValueCountFrequency (%)
False 630
69.1%
True 282
30.9%
2024-11-29T10:04:26.589685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Gpu_Family_gtx
Boolean

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
744 
True
168 
ValueCountFrequency (%)
False 744
81.6%
True 168
 
18.4%
2024-11-29T10:04:26.660223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Gpu_Family_hd graphics
Boolean

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
True
484 
False
428 
ValueCountFrequency (%)
True 484
53.1%
False 428
46.9%
2024-11-29T10:04:26.728864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Gpu_Family_iris
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
899 
True
 
13
ValueCountFrequency (%)
False 899
98.6%
True 13
 
1.4%
2024-11-29T10:04:26.796996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Gpu_Family_radeon
Boolean

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
785 
True
127 
ValueCountFrequency (%)
False 785
86.1%
True 127
 
13.9%
2024-11-29T10:04:26.863104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Company_Acer
Boolean

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
836 
True
 
76
ValueCountFrequency (%)
False 836
91.7%
True 76
 
8.3%
2024-11-29T10:04:26.932031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Company_Apple
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
895 
True
 
17
ValueCountFrequency (%)
False 895
98.1%
True 17
 
1.9%
2024-11-29T10:04:27.002674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
802 
True
110 
ValueCountFrequency (%)
False 802
87.9%
True 110
 
12.1%
2024-11-29T10:04:27.069778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Company_Chuwi
Boolean

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
911 
True
 
1
ValueCountFrequency (%)
False 911
99.9%
True 1
 
0.1%
2024-11-29T10:04:27.139072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
694 
True
218 
ValueCountFrequency (%)
False 694
76.1%
True 218
 
23.9%
2024-11-29T10:04:27.207324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Company_Fujitsu
Boolean

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
911 
True
 
1
ValueCountFrequency (%)
False 911
99.9%
True 1
 
0.1%
2024-11-29T10:04:27.278567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Company_Google
Boolean

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
911 
True
 
1
ValueCountFrequency (%)
False 911
99.9%
True 1
 
0.1%
2024-11-29T10:04:27.345170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Company_HP
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
715 
True
197 
ValueCountFrequency (%)
False 715
78.4%
True 197
 
21.6%
2024-11-29T10:04:27.411557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Company_Huawei
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
910 
True
 
2
ValueCountFrequency (%)
False 910
99.8%
True 2
 
0.2%
2024-11-29T10:04:27.479478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Company_LG
Boolean

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
911 
True
 
1
ValueCountFrequency (%)
False 911
99.9%
True 1
 
0.1%
2024-11-29T10:04:27.545373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
719 
True
193 
ValueCountFrequency (%)
False 719
78.8%
True 193
 
21.2%
2024-11-29T10:04:27.612130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Company_MSI
Boolean

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
876 
True
 
36
ValueCountFrequency (%)
False 876
96.1%
True 36
 
3.9%
2024-11-29T10:04:27.679582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Company_Mediacom
Boolean

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
906 
True
 
6
ValueCountFrequency (%)
False 906
99.3%
True 6
 
0.7%
2024-11-29T10:04:27.747327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Company_Microsoft
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
908 
True
 
4
ValueCountFrequency (%)
False 908
99.6%
True 4
 
0.4%
2024-11-29T10:04:27.814538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Company_Razer
Boolean

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
908 
True
 
4
ValueCountFrequency (%)
False 908
99.6%
True 4
 
0.4%
2024-11-29T10:04:27.880571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Company_Samsung
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
907 
True
 
5
ValueCountFrequency (%)
False 907
99.5%
True 5
 
0.5%
2024-11-29T10:04:27.948038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Company_Toshiba
Boolean

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
875 
True
 
37
ValueCountFrequency (%)
False 875
95.9%
True 37
 
4.1%
2024-11-29T10:04:28.012551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Company_Vero
Boolean

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
911 
True
 
1
ValueCountFrequency (%)
False 911
99.9%
True 1
 
0.1%
2024-11-29T10:04:28.081788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Company_Xiaomi
Boolean

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
910 
True
 
2
ValueCountFrequency (%)
False 910
99.8%
True 2
 
0.2%
2024-11-29T10:04:28.151305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

TypeName_2 in 1 Convertible
Boolean

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
828 
True
84 
ValueCountFrequency (%)
False 828
90.8%
True 84
 
9.2%
2024-11-29T10:04:28.228850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

TypeName_Gaming
Boolean

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
769 
True
143 
ValueCountFrequency (%)
False 769
84.3%
True 143
 
15.7%
2024-11-29T10:04:28.304460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

TypeName_Netbook
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
895 
True
 
17
ValueCountFrequency (%)
False 895
98.1%
True 17
 
1.9%
2024-11-29T10:04:28.373885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

TypeName_Notebook
Boolean

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
True
516 
False
396 
ValueCountFrequency (%)
True 516
56.6%
False 396
43.4%
2024-11-29T10:04:28.444801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

TypeName_Ultrabook
Boolean

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
782 
True
130 
ValueCountFrequency (%)
False 782
85.7%
True 130
 
14.3%
2024-11-29T10:04:28.514450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

TypeName_Workstation
Boolean

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
890 
True
 
22
ValueCountFrequency (%)
False 890
97.6%
True 22
 
2.4%
2024-11-29T10:04:28.584807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

OpSys_Android
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
910 
True
 
2
ValueCountFrequency (%)
False 910
99.8%
True 2
 
0.2%
2024-11-29T10:04:28.652951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

OpSys_Chrome OS
Boolean

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
894 
True
 
18
ValueCountFrequency (%)
False 894
98.0%
True 18
 
2.0%
2024-11-29T10:04:28.722013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

OpSys_Linux
Boolean

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
866 
True
 
46
ValueCountFrequency (%)
False 866
95.0%
True 46
 
5.0%
2024-11-29T10:04:28.792133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

OpSys_Mac OS X
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
906 
True
 
6
ValueCountFrequency (%)
False 906
99.3%
True 6
 
0.7%
2024-11-29T10:04:28.861398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

OpSys_No OS
Boolean

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
866 
True
 
46
ValueCountFrequency (%)
False 866
95.0%
True 46
 
5.0%
2024-11-29T10:04:28.933569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
True
745 
False
167 
ValueCountFrequency (%)
True 745
81.7%
False 167
 
18.3%
2024-11-29T10:04:29.004379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

OpSys_Windows 10 S
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
907 
True
 
5
ValueCountFrequency (%)
False 907
99.5%
True 5
 
0.5%
2024-11-29T10:04:29.074882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

OpSys_Windows 7
Boolean

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
879 
True
 
33
ValueCountFrequency (%)
False 879
96.4%
True 33
 
3.6%
2024-11-29T10:04:29.419404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

OpSys_macOS
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
901 
True
 
11
ValueCountFrequency (%)
False 901
98.8%
True 11
 
1.2%
2024-11-29T10:04:29.488025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Interactions

2024-11-29T10:04:21.926006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:16.234142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:17.034231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:17.982184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:18.756389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:19.625181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:20.339576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:21.029336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:22.011621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:16.314117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:17.151698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:18.070628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:18.843029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:19.705170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:20.419794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:21.112992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:22.101859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:16.396370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:17.286558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:18.166601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:18.932125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:19.797234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:20.505718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:21.201779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:22.201198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:16.488789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:17.437926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:18.265741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:19.176099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:19.891140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:20.598037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:21.296528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:22.304924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:16.578116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:17.599618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:18.374997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:19.267536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:19.993001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:20.690084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:21.395846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:22.399903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:16.769599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:17.713442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:18.468818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:19.357513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:20.081099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:20.773395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:21.480604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:22.484253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:16.845594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:17.797915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:18.566933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:19.444226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:20.163858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:20.852421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:21.568196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:22.583784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:16.930529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:17.886668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:18.660170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:19.532198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:20.251760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:20.938872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T10:04:21.656515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-29T10:04:29.613540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Company_AcerCompany_AppleCompany_AsusCompany_ChuwiCompany_DellCompany_FujitsuCompany_GoogleCompany_HPCompany_HuaweiCompany_LGCompany_LenovoCompany_MSICompany_MediacomCompany_MicrosoftCompany_RazerCompany_SamsungCompany_ToshibaCompany_VeroCompany_XiaomiCpu_Family_IntelCpu_FrequencyCpu_Series_i3Cpu_Series_i5Cpu_Series_i7Cpu_Series_ryzen 1Flash_StorageGpu_Brand_IntelGpu_Brand_NVIDIAGpu_Family_gtxGpu_Family_hd graphicsGpu_Family_irisGpu_Family_radeonGpu_ModelHybridInchesMemory_HDDMemory_SSDOpSys_AndroidOpSys_Chrome OSOpSys_LinuxOpSys_Mac OS XOpSys_No OSOpSys_Windows 10OpSys_Windows 10 SOpSys_Windows 7OpSys_macOSRamResolutionTypeName_2 in 1 ConvertibleTypeName_GamingTypeName_NetbookTypeName_NotebookTypeName_UltrabookTypeName_WorkstationWeight
Company_Acer1.0000.0000.1000.0000.1610.0000.0000.1500.0000.0000.1480.0390.0000.0000.0000.0000.0400.0000.0000.0780.1780.0790.0100.1340.0000.1030.0000.0000.0140.0000.0000.0000.0720.0000.1710.0610.1700.0000.1970.1350.0000.0510.0590.0000.0340.0000.0790.1680.0000.0740.0840.1110.0640.0100.089
Company_Apple0.0001.0000.0200.0000.0590.0000.0000.0530.0000.0000.0520.0000.0000.0000.0000.0000.0000.0000.0000.0000.2950.0160.0800.0440.0000.1930.0780.0770.0440.0000.4270.0000.5360.0000.2200.1110.0000.0000.0000.0000.5400.0000.2790.0000.0000.7640.0000.7160.0000.0350.0000.1450.3250.0000.272
Company_Asus0.1000.0201.0000.0000.2010.0000.0000.1880.0000.0000.1850.0580.0000.0000.0000.0000.0590.0000.0000.0310.2560.0270.1180.0380.1210.0090.1070.1680.1900.0950.0000.0460.0000.0000.1510.1450.0000.0000.0000.0000.0000.0000.0230.0000.0530.0000.1330.0270.0000.1940.0000.1020.0000.0340.141
Company_Chuwi0.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1270.0000.0000.0000.0000.0550.0000.0000.0000.0000.0000.0000.0000.0000.1460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4920.0000.0000.0000.0000.0000.0000.000
Company_Dell0.1610.0590.2010.0001.0000.0000.0000.2890.0000.0000.2850.1020.0000.0000.0000.0000.1040.0000.0000.1190.1470.0180.0930.0000.0000.1030.0560.0370.0000.0530.0000.1690.0620.0000.1650.0520.0070.0000.0400.1790.0000.1190.0140.0000.0000.0380.0120.1490.0000.0000.0000.0000.0000.0000.174
Company_Fujitsu0.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Company_Google0.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1460.0000.0620.0000.1100.0000.0000.0000.0000.0000.0000.0000.0240.1820.0000.0000.0000.0000.0070.0000.000
Company_HP0.1500.0530.1880.0000.2890.0000.0001.0000.0000.0000.2670.0940.0000.0000.0000.0000.0960.0000.0000.0450.1170.0000.0000.0000.0000.0410.0730.1130.1530.0900.0400.0000.0000.0000.0550.1470.0750.0000.0000.1100.0000.0430.0570.0000.1590.0320.1420.0290.0400.1460.0140.0850.0000.1300.134
Company_Huawei0.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0800.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.9970.0000.0000.0000.0000.0740.0000.297
Company_LG0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0890.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0620.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.198
Company_Lenovo0.1480.0520.1850.0000.2850.0000.0000.2670.0000.0001.0000.0920.0000.0000.0000.0000.0940.0000.0000.0070.1630.0000.0000.0000.0000.0000.0290.0000.0530.0500.0390.0070.0820.0400.1940.0610.0020.0520.0300.1080.0000.3020.0540.0000.0000.0300.0000.0810.1230.0640.0250.0700.1020.0000.000
Company_MSI0.0390.0000.0580.0000.1020.0000.0000.0940.0000.0000.0921.0000.0000.0000.0000.0000.0000.0000.0000.0070.2610.0550.0920.2050.0000.0170.2140.2950.4180.2070.0000.0660.1540.0000.3000.2390.1710.0000.0000.0070.0000.0070.0820.0000.0000.0000.2500.1110.0440.4610.0000.2230.0670.0000.321
Company_Mediacom0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.2380.0000.0250.0410.0000.0000.0510.0220.0000.0540.0000.0000.0430.0000.0960.0460.0770.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.124
Company_Microsoft0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.3410.0000.0500.0000.1280.0180.0000.0000.0000.0000.0000.0000.1140.7810.0000.0000.0000.3940.0000.0000.0000.0490.1350.0000.084
Company_Razer0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0360.0000.0000.0550.0000.0000.0000.0310.0680.0000.0000.0000.0500.0000.1060.0180.1490.0000.0000.0000.0000.0000.0000.0000.0000.0000.1370.0210.0000.0790.0000.0490.0000.0000.000
Company_Samsung0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0850.0000.0130.0310.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.6360.0350.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0610.0680.0000.076
Company_Toshiba0.0400.0000.0590.0000.1040.0000.0000.0960.0000.0000.0940.0000.0000.0000.0000.0001.0000.0000.0000.0100.1630.0000.0830.0000.0000.0190.1330.0770.0840.1390.0000.0670.0000.0000.1220.1570.0280.0000.0000.0100.0000.0100.0680.0000.0000.0000.0000.0000.0450.0740.0000.0660.0390.0000.163
Company_Vero0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.1810.0000.0000.0000.0000.0550.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Company_Xiaomi0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1460.0600.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Cpu_Family_Intel0.0780.0000.0310.0000.1190.0000.0000.0450.0000.0000.0070.0070.0000.0000.0000.0000.0100.0000.0001.0000.3650.0670.1500.1810.2030.0000.2460.1450.0980.2380.0000.5650.1960.0490.1150.0600.0630.0000.0000.0250.0000.0000.0690.0000.0000.0000.0580.1250.0560.0390.0000.1530.0640.0000.166
Cpu_Frequency0.1780.2950.2560.1270.1470.0000.1810.1170.0000.0890.1630.2610.2380.0000.0360.0850.1630.1810.0000.3651.0000.7030.6800.7870.2820.4750.5130.5360.5940.5140.2690.2790.2770.0000.3060.1610.2950.2740.3290.1030.1770.1440.1730.0000.1510.3090.4710.2930.2360.5890.2370.4410.2120.1700.346
Cpu_Series_i30.0790.0160.0270.0000.0180.0000.0000.0000.0000.0000.0000.0550.0000.0000.0000.0000.0000.0000.0000.0670.7031.0000.2420.2880.0000.0730.0740.1120.1540.0860.0000.0220.1320.0000.1730.1890.2650.0000.0200.0760.0000.1260.0470.0000.0260.0000.1570.2550.0510.1460.0160.2740.1270.0310.209
Cpu_Series_i50.0100.0800.1180.0000.0930.0000.0000.0000.0000.0000.0000.0920.0250.0000.0000.0130.0830.0000.0000.1500.6800.2421.0000.5630.0000.1180.2160.1780.1530.2040.0000.0640.2160.0000.1990.1200.2280.0000.0830.0000.0000.0000.0000.0000.0350.0780.2880.1510.0000.1690.0400.1700.0000.0960.135
Cpu_Series_i70.1340.0440.0380.0000.0000.0000.0000.0000.0000.0000.0000.2050.0410.0000.0550.0310.0000.0000.0000.1810.7870.2880.5631.0000.0000.1720.3240.4360.4090.3290.0000.1150.1850.0000.2370.2310.4360.0000.0860.0230.0000.0630.1020.0000.0000.0210.5110.4410.0300.3870.0820.4480.1590.1370.291
Cpu_Series_ryzen 10.0000.0000.1210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2030.2820.0000.0000.0001.0000.0000.0290.0000.0000.0260.0000.1100.0000.0000.1170.0580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1020.0000.0320.0000.0000.371
Flash_Storage0.1030.1930.0090.0550.1030.0000.0000.0410.0000.0000.0000.0170.0000.0000.0000.0000.0190.0550.0000.0000.4750.0730.1180.1720.0001.0000.1950.1440.1040.1930.0000.0700.3520.0000.5590.2030.3100.1380.4960.0310.3030.0310.1840.0000.0090.0000.0750.2600.0720.0930.2640.0400.0000.0000.307
Gpu_Brand_Intel0.0000.0780.1070.0000.0560.0000.0000.0730.0000.0000.0290.2140.0510.0290.0000.0000.1330.0000.0000.2460.5130.0740.2160.3240.0290.1951.0000.7310.5180.9670.0950.4370.2140.0290.6060.4710.1080.0000.1170.0000.0510.0000.0740.0000.0240.0380.2940.3070.2160.4690.1130.0280.2600.1470.607
Gpu_Brand_NVIDIA0.0000.0770.1680.0000.0370.0000.0000.1130.0000.0000.0000.2950.0220.0000.0310.0060.0770.0000.0300.1450.5360.1120.1780.4360.0000.1440.7311.0000.7070.7090.0620.2640.0400.0000.5090.4140.1770.0000.0800.0520.0220.0320.0930.0060.0000.0540.3970.3570.1480.6150.0770.2620.1780.1780.556
Gpu_Family_gtx0.0140.0440.1900.0000.0000.0000.0000.1530.0000.0000.0530.4180.0000.0000.0680.0000.0840.0000.0000.0980.5940.1540.1530.4090.0000.1040.5180.7071.0000.5020.0310.1840.3390.0000.4390.4530.2350.0000.0470.0000.0000.0000.1140.0000.0780.0220.4630.3010.1230.8720.0440.3960.1700.0570.588
Gpu_Family_hd graphics0.0000.0000.0950.0000.0530.0000.0000.0900.0000.0000.0500.2070.0540.0000.0000.0000.1390.0000.0000.2380.5140.0860.2040.3290.0260.1930.9670.7090.5021.0000.1140.4230.1930.0240.5690.4470.1050.0000.1210.0000.0130.0000.0210.0090.0330.0330.2960.2900.2250.4550.1170.0710.1830.1420.568
Gpu_Family_iris0.0000.4270.0000.0000.0000.0000.0000.0400.0000.0000.0390.0000.0000.3410.0000.0000.0000.0000.0000.0000.2690.0000.0000.0000.0000.0000.0950.0620.0310.1141.0000.0110.0580.0000.1930.0930.0700.0000.0000.0000.0340.0000.1670.3030.0000.4520.0000.7150.0000.0210.0000.1240.2800.0000.188
Gpu_Family_radeon0.0000.0000.0460.0000.1690.0000.0000.0000.0000.0000.0070.0660.0000.0000.0000.0000.0670.0000.0000.5650.2790.0220.0640.1150.1100.0700.4370.2640.1840.4230.0111.0000.3510.0330.2590.1420.1250.0000.0310.1120.0000.0000.0000.0000.0410.0000.0650.1350.0950.1300.0290.2830.1190.0410.261
Gpu_Model0.0720.5360.0000.0000.0620.0000.0000.0000.0000.0000.0820.1540.0430.0500.0500.0000.0000.0000.0000.1960.2770.1320.2160.1850.0000.3520.2140.0400.3390.1930.0580.3511.0000.000-0.0450.0450.2650.0000.0930.0730.7330.0980.2700.0000.1610.1370.3300.2410.0380.3430.0680.3090.2020.1110.001
Hybrid0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0400.0000.0000.0000.0000.0000.0000.0000.0000.0490.0000.0000.0000.0000.0000.0000.0290.0000.0000.0240.0000.0330.0001.0000.0000.0630.0660.0000.0000.0000.0000.0000.0000.0000.0690.0000.0000.0000.0000.0230.0000.0000.0000.0000.060
Inches0.1710.2200.1510.1460.1650.0000.1460.0550.0800.0000.1940.3000.0960.1280.1060.6360.1220.0000.0000.1150.3060.1730.1990.2370.1170.5590.6060.5090.4390.5690.1930.259-0.0450.0001.0000.572-0.1610.8110.4510.1510.1390.1180.1880.1510.1930.1750.142-0.1090.3950.4610.6810.4580.5270.0640.873
Memory_HDD0.0610.1110.1450.0000.0520.0000.0000.1470.0000.0000.0610.2390.0460.0180.0180.0350.1570.0000.0000.0600.1610.1890.1200.2310.0580.2030.4710.4140.4530.4470.0930.1420.0450.0630.5721.000-0.5180.0000.1150.1380.0460.1450.0390.0350.1190.0820.039-0.1860.2160.4520.1110.2730.3460.0430.594
Memory_SSD0.1700.0000.0000.0000.0070.0000.0620.0750.0000.0620.0020.1710.0770.0000.1490.0000.0280.0000.0000.0630.2950.2650.2280.4360.0000.3100.1080.1770.2350.1050.0700.1250.2650.066-0.161-0.5181.0000.0000.1540.1550.0770.1350.2060.0000.0640.0730.6010.5660.1720.2260.0630.4190.3440.065-0.186
OpSys_Android0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0520.0000.0000.0000.0000.0000.0000.0000.0000.0000.2740.0000.0000.0000.0000.1380.0000.0000.0000.0000.0000.0000.0000.0000.8110.0000.0001.0000.0000.0000.0000.0000.0600.0000.0000.0000.0000.0000.1010.0000.0000.0000.0000.0000.297
OpSys_Chrome OS0.1970.0000.0000.0000.0400.0000.1100.0000.0000.0000.0300.0000.0000.0000.0000.0270.0000.0000.0000.0000.3290.0200.0830.0860.0000.4960.1170.0800.0470.1210.0000.0310.0930.0000.4510.1150.1540.0001.0000.0000.0000.0000.2880.0000.0000.0000.0000.1340.0700.0380.2990.0480.0000.0000.181
OpSys_Linux0.1350.0000.0000.0000.1790.0000.0000.1100.0000.0000.1080.0070.0000.0000.0000.0000.0100.0000.0000.0250.1030.0760.0000.0230.0000.0310.0000.0520.0000.0000.0000.1120.0730.0000.1510.1380.1550.0000.0001.0000.0000.0250.4790.0000.0000.0000.0520.1230.0560.0390.0000.1430.0480.0000.161
OpSys_Mac OS X0.0000.5400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1770.0000.0000.0000.0000.3030.0510.0220.0000.0130.0340.0000.7330.0000.1390.0460.0770.0000.0000.0001.0000.0000.1510.0000.0000.0000.0000.2490.0000.0000.0000.0720.1770.0000.251
OpSys_No OS0.0510.0000.0000.0000.1190.0000.0000.0430.0000.0000.3020.0070.0000.0000.0000.0000.0100.0000.1460.0000.1440.1260.0000.0630.0000.0310.0000.0320.0000.0000.0000.0000.0980.0000.1180.1450.1350.0000.0000.0250.0001.0000.4790.0000.0000.0000.0450.0430.0560.0000.0000.1320.0640.0000.124
OpSys_Windows 100.0590.2790.0230.0000.0140.0000.0000.0570.0000.0000.0540.0820.0000.1140.0000.0000.0680.0000.0600.0690.1730.0470.0000.1020.0000.1840.0740.0930.1140.0210.1670.0000.2700.0000.1880.0390.2060.0600.2880.4790.1510.4791.0000.1340.4010.2180.1160.2870.0590.1340.0630.0090.0800.0550.087
OpSys_Windows 10 S0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.7810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0090.3030.0000.0000.0000.1510.0350.0000.0000.0000.0000.0000.0000.1341.0000.0000.0000.0000.3490.0000.0000.0000.0220.1140.0000.057
OpSys_Windows 70.0340.0000.0530.0000.0000.0000.0000.1590.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1510.0260.0350.0000.0000.0090.0240.0000.0780.0330.0000.0410.1610.0690.1930.1190.0640.0000.0000.0000.0000.0000.4010.0001.0000.0000.0000.0000.0000.0680.0000.0520.0730.2550.000
OpSys_macOS0.0000.7640.0000.0000.0380.0000.0000.0320.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.0000.0000.3090.0000.0780.0210.0000.0000.0380.0540.0220.0330.4520.0000.1370.0000.1750.0820.0730.0000.0000.0000.0000.0000.2180.0000.0001.0000.0000.8000.0000.0070.0000.1110.2550.0000.152
Ram0.0790.0000.1330.0000.0120.0000.0240.1420.0000.0000.0000.2500.0000.0000.1370.0000.0000.0000.0000.0580.4710.1570.2880.5110.0000.0750.2940.3970.4630.2960.0000.0650.3300.0000.1420.0390.6010.0000.0000.0520.0000.0450.1160.0000.0000.0001.0000.5640.0000.4760.0000.3750.0000.0170.181
Resolution0.1680.7160.0270.4920.1490.0000.1820.0290.9970.0000.0810.1110.0000.3940.0210.0000.0000.0000.0000.1250.2930.2550.1510.4410.0000.2600.3070.3570.3010.2900.7150.1350.2410.000-0.109-0.1860.5660.0000.1340.1230.2490.0430.2870.3490.0000.8000.5641.0000.1770.2790.1720.4240.4150.104-0.105
TypeName_2 in 1 Convertible0.0000.0000.0000.0000.0000.0000.0000.0400.0000.0000.1230.0440.0000.0000.0000.0000.0450.0000.0000.0560.2360.0510.0000.0300.0000.0720.2160.1480.1230.2250.0000.0950.0380.0000.3950.2160.1720.1010.0700.0560.0000.0560.0590.0000.0000.0000.0000.1771.0000.1280.0000.3580.1200.0180.285
TypeName_Gaming0.0740.0350.1940.0000.0000.0000.0000.1460.0000.0000.0640.4610.0000.0000.0790.0000.0740.0000.0000.0390.5890.1460.1690.3870.1020.0930.4690.6150.8720.4550.0210.1300.3430.0230.4610.4520.2260.0000.0380.0390.0000.0000.1340.0000.0680.0070.4760.2790.1281.0000.0350.4880.1680.0480.627
TypeName_Netbook0.0840.0000.0000.0000.0000.0000.0000.0140.0000.0000.0250.0000.0000.0000.0000.0300.0000.0000.0000.0000.2370.0160.0400.0820.0000.2640.1130.0770.0440.1170.0000.0290.0680.0000.6810.1110.0630.0000.2990.0000.0000.0000.0630.0000.0000.0000.0000.1720.0000.0351.0000.1450.0300.0000.173
TypeName_Notebook0.1110.1450.1020.0000.0000.0000.0000.0850.0000.0000.0700.2230.0000.0490.0490.0610.0660.0000.0000.1530.4410.2740.1700.4480.0320.0400.0280.2620.3960.0710.1240.2830.3090.0000.4580.2730.4190.0000.0480.1430.0720.1320.0090.0220.0520.1110.3750.4240.3580.4880.1451.0000.4610.1690.532
TypeName_Ultrabook0.0640.3250.0000.0000.0000.0000.0070.0000.0740.0070.1020.0670.0000.1350.0000.0680.0390.0000.0000.0640.2120.1270.0000.1590.0000.0000.2600.1780.1700.1830.2800.1190.2020.0000.5270.3460.3440.0000.0000.0480.1770.0640.0800.1140.0730.2550.0000.4150.1200.1680.0300.4611.0000.0430.550
TypeName_Workstation0.0100.0000.0340.0000.0000.0000.0000.1300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1700.0310.0960.1370.0000.0000.1470.1780.0570.1420.0000.0410.1110.0000.0640.0430.0650.0000.0000.0000.0000.0000.0550.0000.2550.0000.0170.1040.0180.0480.0000.1690.0431.0000.117
Weight0.0890.2720.1410.0000.1740.0000.0000.1340.2970.1980.0000.3210.1240.0840.0000.0760.1630.0000.0000.1660.3460.2090.1350.2910.3710.3070.6070.5560.5880.5680.1880.2610.0010.0600.8730.594-0.1860.2970.1810.1610.2510.1240.0870.0570.0000.1520.181-0.1050.2850.6270.1730.5320.5500.1171.000

Missing values

2024-11-29T10:04:22.900511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-29T10:04:23.321992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

InchesRamWeightResolutionMemory_SSDMemory_HDDFlash_StorageHybridCpu_CoresCpu_FrequencyGpu_ModelCpu_Family_IntelCpu_Series_i3Cpu_Series_i5Cpu_Series_i7Cpu_Series_ryzen 1Gpu_Brand_IntelGpu_Brand_NVIDIAGpu_Family_gtxGpu_Family_hd graphicsGpu_Family_irisGpu_Family_radeonCompany_AcerCompany_AppleCompany_AsusCompany_ChuwiCompany_DellCompany_FujitsuCompany_GoogleCompany_HPCompany_HuaweiCompany_LGCompany_LenovoCompany_MSICompany_MediacomCompany_MicrosoftCompany_RazerCompany_SamsungCompany_ToshibaCompany_VeroCompany_XiaomiTypeName_2 in 1 ConvertibleTypeName_GamingTypeName_NetbookTypeName_NotebookTypeName_UltrabookTypeName_WorkstationOpSys_AndroidOpSys_Chrome OSOpSys_LinuxOpSys_Mac OS XOpSys_No OSOpSys_Windows 10OpSys_Windows 10 SOpSys_Windows 7OpSys_macOS
012.541.202073600.0001000.9515TrueFalseFalseFalseFalseTrueFalseFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalse
117.383.202073600.025610240003.2580FalseFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse
215.641.852073600.0010240002.7620TrueFalseFalseTrueFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse
313.3161.295760000.051200002.5640TrueFalseFalseTrueFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse
415.642.322073600.0000102.5520TrueFalseFalseTrueFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalse
513.341.202073600.03200001.1500TrueFalseFalseFalseFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse
615.642.202073600.005000001.1505TrueFalseFalseFalseFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse
715.6162.652073600.025610240002.81060TrueFalseFalseTrueFalseFalseTrueTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse
814.081.703686400.051200002.80TrueFalseFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse
917.3164.423686400.025610240002.91070TrueFalseFalseTrueFalseFalseTrueTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse
InchesRamWeightResolutionMemory_SSDMemory_HDDFlash_StorageHybridCpu_CoresCpu_FrequencyGpu_ModelCpu_Family_IntelCpu_Series_i3Cpu_Series_i5Cpu_Series_i7Cpu_Series_ryzen 1Gpu_Brand_IntelGpu_Brand_NVIDIAGpu_Family_gtxGpu_Family_hd graphicsGpu_Family_irisGpu_Family_radeonCompany_AcerCompany_AppleCompany_AsusCompany_ChuwiCompany_DellCompany_FujitsuCompany_GoogleCompany_HPCompany_HuaweiCompany_LGCompany_LenovoCompany_MSICompany_MediacomCompany_MicrosoftCompany_RazerCompany_SamsungCompany_ToshibaCompany_VeroCompany_XiaomiTypeName_2 in 1 ConvertibleTypeName_GamingTypeName_NetbookTypeName_NotebookTypeName_UltrabookTypeName_WorkstationOpSys_AndroidOpSys_Chrome OSOpSys_LinuxOpSys_Mac OS XOpSys_No OSOpSys_Windows 10OpSys_Windows 10 SOpSys_Windows 7OpSys_macOS
90215.642.102073600.005000002.0520TrueTrueFalseFalseFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalse
90314.081.702073600.025600002.3520TrueFalseTrueFalseFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse
90413.381.602073600.025600002.3520TrueFalseTrueFalseFalseTrueFalseFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse
90515.682.002073600.025610240002.7620TrueFalseFalseTrueFalseTrueFalseFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse
90615.682.302073600.025600002.5620TrueFalseTrueFalseFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse
90715.642.181049088.0010240002.50TrueFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse
90815.662.402073600.0010240002.4430FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse
90915.682.202073600.012810240002.51050TrueFalseTrueFalseFalseFalseTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse
91015.642.191049088.005000002.00FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse
91115.682.502073600.012810240002.81060TrueFalseFalseTrueFalseFalseTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse

Duplicate rows

Most frequently occurring

InchesRamWeightResolutionMemory_SSDMemory_HDDFlash_StorageHybridCpu_CoresCpu_FrequencyGpu_ModelCpu_Family_IntelCpu_Series_i3Cpu_Series_i5Cpu_Series_i7Cpu_Series_ryzen 1Gpu_Brand_IntelGpu_Brand_NVIDIAGpu_Family_gtxGpu_Family_hd graphicsGpu_Family_irisGpu_Family_radeonCompany_AcerCompany_AppleCompany_AsusCompany_ChuwiCompany_DellCompany_FujitsuCompany_GoogleCompany_HPCompany_HuaweiCompany_LGCompany_LenovoCompany_MSICompany_MediacomCompany_MicrosoftCompany_RazerCompany_SamsungCompany_ToshibaCompany_VeroCompany_XiaomiTypeName_2 in 1 ConvertibleTypeName_GamingTypeName_NetbookTypeName_NotebookTypeName_UltrabookTypeName_WorkstationOpSys_AndroidOpSys_Chrome OSOpSys_LinuxOpSys_Mac OS XOpSys_No OSOpSys_Windows 10OpSys_Windows 10 SOpSys_Windows 7OpSys_macOS# duplicates
2415.662.301049088.0010240002.40620TrueTrueFalseFalseFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse4
1314.041.802073600.012800002.50520TrueFalseFalseTrueFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse3
1915.642.201049088.005000001.600TrueFalseFalseFalseFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalse3
2215.662.191049088.0010240002.500TrueFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse3
2915.682.202073600.012810240002.801050TrueFalseFalseTrueFalseFalseTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse3
3215.682.301049088.0010240002.700TrueFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalse3
010.140.692304000.0001001.44400TrueFalseFalseFalseFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalse2
111.621.171049088.0001001.60400TrueFalseFalseFalseFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse2
211.641.251049088.0001001.60400TrueFalseFalseFalseFalseTrueFalseFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalse2
313.381.052073600.025600002.50620TrueFalseTrueFalseFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse2